Resumen
In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.
| Idioma original | English |
|---|---|
| Título de la publicación alojada | GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference |
| Editores | Tobias Friedrich |
| Editorial | Association for Computing Machinery, Inc |
| Páginas | 389-396 |
| Número de páginas | 8 |
| ISBN (versión digital) | 9781450342063 |
| DOI | |
| Estado | Published - 20 jul 2016 |
| Evento | 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 - Denver, United States Duración: 20 jul 2016 → 24 jul 2016 |
Conference
| Conference | 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 |
|---|---|
| País | United States |
| Ciudad | Denver |
| Período | 20/07/16 → 24/07/16 |
Huella dactilar
ASJC Scopus subject areas
- Computer Science Applications
- Computational Theory and Mathematics
- Software
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Ants can learn from the opposite. / Rojas-Morales, Nicolás; María-Cristina, Riff R.; Montero, Elizabeth.
GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. ed. / Tobias Friedrich. Association for Computing Machinery, Inc, 2016. p. 389-396.Resultado de la investigación: Conference contribution
TY - GEN
T1 - Ants can learn from the opposite
AU - Rojas-Morales, Nicolás
AU - María-Cristina, Riff R.
AU - Montero, Elizabeth
PY - 2016/7/20
Y1 - 2016/7/20
N2 - In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.
AB - In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.
KW - Ant algorithms
KW - Antipheromone
KW - Negative pheromone
KW - Opposite learning strategies
UR - http://www.scopus.com/inward/record.url?scp=84985914541&partnerID=8YFLogxK
U2 - 10.1145/2908812.2908927
DO - 10.1145/2908812.2908927
M3 - Conference contribution
AN - SCOPUS:84985914541
SP - 389
EP - 396
BT - GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
A2 - Friedrich, Tobias
PB - Association for Computing Machinery, Inc
ER -